ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2018
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) proteomic profiling of cerebrospinal fluid in the diagnosis of enteroviral meningitis: a proof-of-principle study.
Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Risk, Predictive, and Preventive Factors for Noninfectious Ventriculitis and External Ventricular Drain Infection.Neurocritical care · 2024Article
- Effect of Tryptic Digestion on Sensitivity and Specificity in MALDI-TOF-Based Molecular Diagnostics through Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Ventriculitis: Infection or Inflammation.Antibiotics (Basel, Switzerland) · 2021Review
- Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) Analysis for the Identification of Pathogenic Microorganisms: A Review.Microorganisms · 2021Review
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
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Abstract
The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for diagnosing viral infections by directly testing clinical specimens has not previously been explored. In this proof-of-principle study, we tested the hypothesis that proteomic profiling of cerebrospinal fluid (CSF) by mass spectrometry may be useful in the diagnosis of enteroviral (EV) meningitis. A total of 114 cryopreserved CSF samples were analyzed, of which 47 were positive for EV and 67 were negative. Total CSF proteins were precipitated and subjected to MALDI-TOF-MS analysis in a low (2-20 kDa) molecular weight range using a MicroFlex LT mass spectrometer. The whole data set was randomly split into a training set (n = 76 specimens) and a validation set (n = 38 samples). Backward/forward stepwise logistic regression analyses identified 30 peaks that were differentially present in EV-positive and EV-negative specimens. These were used to build a model which displayed an overall classification accuracy of 93%. The discriminative ability of the model was confirmed by using a validation sample set (overall accuracy 83%). In fact, the model was able to correctly classify 61 out of 67 EV-negative samples and 42 out of 47 EV-positive specimens. EV meningitis is associated with a distinctive protein profile that may be directly detectable in CSF specimens by MALDI-TOF-MS.
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